Improved intraindividual variability in cognitive performance following cognitive and exercise training in older adults
Bibliographic record
Abstract
Abstract Objective: Increased intraindividual variability (IIV) of cognitive performance is a marker of cognitive decline in older adults. Whether computerized cognitive training (CCT) and aerobic exercise counteracts cognitive decline by reducing IIV is unknown. We investigated the effects of CCT with or without aerobic exercise on IIV in older adults. Methods: This was a secondary analysis of an 8-week randomized controlled trial. Older adults (aged 65–85 years) were randomized to CCT alone (n = 41), CCT with aerobic exercise (n = 41), or an active control group (n = 42). The CCT group trained using the Fit Brains® platform 3×/week for 1 hr (plus 3×/week of home-based training). The CCT with aerobic exercise group received 15 min of walking plus 45 min of Fit Brains® 3×/week (plus 3×/week of home-based training). The control group received sham exercise and cognitive training (3×/week for 1 hr). We computed reaction time IIV from the Dimensional Change Card Sort Test, Flanker Inhibitory Control and Attention Test (Flanker), and Pattern Comparison Processing Speed Test (PACPS). Results: Compared with the control group, IIV reduced in a processing speed task (PACPS) following CCT alone (mean difference [95% confidence interval]: −0.144 [−0.255 to −0.034], p < 0.01) and CCT with aerobic exercise (−0.113 [−0.225 to −0.001], p < 0.05). Attention (Flanker congruent) IIV was reduced only after CCT with aerobic exercise (−0.130 [−0.242 to −0.017], p < 0.05). Conclusions: A CCT program promoted cognitive health via reductions in IIV of cognitive performance and combining it with aerobic exercise may result in broader benefits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".